Overview

Dataset statistics

Number of variables41
Number of observations24301
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory328.0 B

Variable types

Numeric21
Categorical20

Alerts

int_rate has a high cardinality: 361 distinct values High cardinality
emp_title has a high cardinality: 19414 distinct values High cardinality
issue_d has a high cardinality: 55 distinct values High cardinality
desc has a high cardinality: 16214 distinct values High cardinality
title has a high cardinality: 12699 distinct values High cardinality
zip_code has a high cardinality: 783 distinct values High cardinality
earliest_cr_line has a high cardinality: 495 distinct values High cardinality
revol_util has a high cardinality: 1059 distinct values High cardinality
last_pymnt_d has a high cardinality: 102 distinct values High cardinality
last_credit_pull_d has a high cardinality: 102 distinct values High cardinality
loan_amnt is highly correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 6 other fieldsHigh correlation
loan_status is highly correlated with recoveries and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
total_acc is highly correlated with open_accHigh correlation
total_pymnt is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_pymnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_prncp is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_int is highly correlated with loan_amnt and 6 other fieldsHigh correlation
recoveries is highly correlated with loan_status and 1 other fieldsHigh correlation
collection_recovery_fee is highly correlated with loan_status and 1 other fieldsHigh correlation
last_pymnt_amnt is highly correlated with total_pymnt and 1 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
loan_amnt is highly correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
term is highly correlated with total_rec_intHigh correlation
installment is highly correlated with loan_amnt and 6 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
total_acc is highly correlated with open_accHigh correlation
total_pymnt is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_pymnt_inv is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_prncp is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_int is highly correlated with loan_amnt and 7 other fieldsHigh correlation
recoveries is highly correlated with collection_recovery_feeHigh correlation
collection_recovery_fee is highly correlated with recoveriesHigh correlation
last_pymnt_amnt is highly correlated with total_pymnt and 2 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
loan_amnt is highly correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 6 other fieldsHigh correlation
loan_status is highly correlated with recoveries and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
total_acc is highly correlated with open_accHigh correlation
total_pymnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_pymnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_prncp is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_int is highly correlated with loan_amnt and 6 other fieldsHigh correlation
recoveries is highly correlated with loan_status and 1 other fieldsHigh correlation
collection_recovery_fee is highly correlated with loan_status and 1 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
sub_grade is highly correlated with gradeHigh correlation
grade is highly correlated with sub_gradeHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
loan_amnt is highly correlated with funded_amnt and 7 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 7 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 7 other fieldsHigh correlation
term is highly correlated with sub_grade and 1 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 7 other fieldsHigh correlation
grade is highly correlated with sub_gradeHigh correlation
sub_grade is highly correlated with term and 2 other fieldsHigh correlation
loan_status is highly correlated with total_rec_prncpHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
total_acc is highly correlated with open_accHigh correlation
total_pymnt is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_pymnt_inv is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_prncp is highly correlated with loan_amnt and 8 other fieldsHigh correlation
total_rec_int is highly correlated with loan_amnt and 8 other fieldsHigh correlation
recoveries is highly correlated with collection_recovery_feeHigh correlation
collection_recovery_fee is highly correlated with recoveriesHigh correlation
last_pymnt_amnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
annual_inc is highly skewed (γ1 = 36.43261349) Skewed
collection_recovery_fee is highly skewed (γ1 = 22.72682334) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
delinq_2yrs has 21691 (89.3%) zeros Zeros
inq_last_6mths has 11833 (48.7%) zeros Zeros
revol_bal has 606 (2.5%) zeros Zeros
total_rec_late_fee has 23074 (95.0%) zeros Zeros
recoveries has 21677 (89.2%) zeros Zeros
collection_recovery_fee has 21936 (90.3%) zeros Zeros

Reproduction

Analysis started2021-10-24 13:23:30.390234
Analysis finished2021-10-24 13:25:03.606381
Duration1 minute and 33.22 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct24301
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12505.21843
Minimum0
Maximum24998
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile1248
Q16241
median12510
Q318761
95-th percentile23758
Maximum24998
Range24998
Interquartile range (IQR)12520

Descriptive statistics

Standard deviation7222.96211
Coefficient of variation (CV)0.5775958375
Kurtosis-1.200918075
Mean12505.21843
Median Absolute Deviation (MAD)6260
Skewness-0.0008194499169
Sum303889313
Variance52171181.65
MonotonicityStrictly increasing
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
231821
 
< 0.1%
108801
 
< 0.1%
88331
 
< 0.1%
149781
 
< 0.1%
129311
 
< 0.1%
26921
 
< 0.1%
6451
 
< 0.1%
47431
 
< 0.1%
190841
 
< 0.1%
Other values (24291)24291
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
249981
< 0.1%
249971
< 0.1%
249961
< 0.1%
249951
< 0.1%
249941
< 0.1%
249931
< 0.1%
249921
< 0.1%
249911
< 0.1%
249901
< 0.1%
249891
< 0.1%

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct762
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11108.38443
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7295.67766
Coefficient of variation (CV)0.6567721622
Kurtosis0.8052426437
Mean11108.38443
Median Absolute Deviation (MAD)5000
Skewness1.058928957
Sum269944850
Variance53226912.52
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100001787
 
7.4%
120001454
 
6.0%
50001239
 
5.1%
60001211
 
5.0%
150001162
 
4.8%
20000988
 
4.1%
8000955
 
3.9%
25000847
 
3.5%
4000678
 
2.8%
3000641
 
2.6%
Other values (752)13339
54.9%
ValueCountFrequency (%)
5002
 
< 0.1%
9002
 
< 0.1%
9501
 
< 0.1%
1000164
0.7%
10503
 
< 0.1%
11003
 
< 0.1%
11251
 
< 0.1%
11501
 
< 0.1%
120085
0.3%
12506
 
< 0.1%
ValueCountFrequency (%)
35000353
1.5%
348002
 
< 0.1%
345251
 
< 0.1%
344754
 
< 0.1%
342001
 
< 0.1%
3400011
 
< 0.1%
339505
 
< 0.1%
336003
 
< 0.1%
335001
 
< 0.1%
332501
 
< 0.1%

funded_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct929
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10836.68882
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7032.562475
Coefficient of variation (CV)0.6489586065
Kurtosis1.006439294
Mean10836.68882
Median Absolute Deviation (MAD)4600
Skewness1.089169338
Sum263342375
Variance49456934.97
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100001734
 
7.1%
120001400
 
5.8%
50001233
 
5.1%
60001205
 
5.0%
150001088
 
4.5%
8000947
 
3.9%
20000868
 
3.6%
25000691
 
2.8%
4000677
 
2.8%
3000637
 
2.6%
Other values (919)13821
56.9%
ValueCountFrequency (%)
5002
 
< 0.1%
9002
 
< 0.1%
9501
 
< 0.1%
1000164
0.7%
10503
 
< 0.1%
11003
 
< 0.1%
11251
 
< 0.1%
11501
 
< 0.1%
120087
0.4%
12506
 
< 0.1%
ValueCountFrequency (%)
35000295
1.2%
348001
 
< 0.1%
345251
 
< 0.1%
344753
 
< 0.1%
342501
 
< 0.1%
3400010
 
< 0.1%
339504
 
< 0.1%
336003
 
< 0.1%
335001
 
< 0.1%
332501
 
< 0.1%

funded_amnt_inv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5415
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10275.87399
Minimum0
Maximum35000
Zeros87
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile1963.54
Q15000
median8925
Q314025
95-th percentile24470.7045
Maximum35000
Range35000
Interquartile range (IQR)9025

Descriptive statistics

Standard deviation6965.327422
Coefficient of variation (CV)0.6778330901
Kurtosis1.141229871
Mean10275.87399
Median Absolute Deviation (MAD)4125
Skewness1.11382658
Sum249714013.9
Variance48515786.09
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000813
 
3.3%
5000791
 
3.3%
6000762
 
3.1%
12000659
 
2.7%
8000526
 
2.2%
3000500
 
2.1%
4000475
 
2.0%
15000394
 
1.6%
7000377
 
1.6%
2000277
 
1.1%
Other values (5405)18727
77.1%
ValueCountFrequency (%)
087
0.4%
0.0006546071
 
< 0.1%
0.0018676961
 
< 0.1%
0.0019630931
 
< 0.1%
0.0022835981
 
< 0.1%
0.0023730581
 
< 0.1%
0.0029150211
 
< 0.1%
0.0030844911
 
< 0.1%
0.003100161
 
< 0.1%
0.004353681
 
< 0.1%
ValueCountFrequency (%)
3500075
0.3%
34993.655391
 
< 0.1%
34993.325711
 
< 0.1%
34993.196961
 
< 0.1%
34977.346741
 
< 0.1%
34975.816361
 
< 0.1%
3497548
0.2%
34972.352451
 
< 0.1%
34972.121831
 
< 0.1%
34972.059821
 
< 0.1%

term
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
36
18203 
60
6098 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters48602
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row36
3rd row36
4th row60
5th row36

Common Values

ValueCountFrequency (%)
3618203
74.9%
606098
 
25.1%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
3618203
74.9%
606098
 
25.1%

Most occurring characters

ValueCountFrequency (%)
624301
50.0%
318203
37.5%
06098
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48602
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
624301
50.0%
318203
37.5%
06098
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common48602
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
624301
50.0%
318203
37.5%
06098
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII48602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
624301
50.0%
318203
37.5%
06098
 
12.5%

int_rate
Categorical

HIGH CARDINALITY

Distinct361
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
10.99%
 
575
7.51%
 
496
11.49%
 
479
7.88%
 
476
13.49%
 
470
Other values (356)
21805 

Length

Max length6
Median length6
Mean length5.690177359
Min length5

Characters and Unicode

Total characters138277
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.1%

Sample

1st row7.51%
2nd row8.94%
3rd row13.57%
4th row17.49%
5th row8.94%

Common Values

ValueCountFrequency (%)
10.99%575
 
2.4%
7.51%496
 
2.0%
11.49%479
 
2.0%
7.88%476
 
2.0%
13.49%470
 
1.9%
7.49%425
 
1.7%
9.99%357
 
1.5%
5.42%355
 
1.5%
7.90%348
 
1.4%
11.71%341
 
1.4%
Other values (351)19979
82.2%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
10.99575
 
2.4%
7.51496
 
2.0%
11.49479
 
2.0%
7.88476
 
2.0%
13.49470
 
1.9%
7.49425
 
1.7%
9.99357
 
1.5%
5.42355
 
1.5%
7.90348
 
1.4%
11.71341
 
1.4%
Other values (351)19979
82.2%

Most occurring characters

ValueCountFrequency (%)
%24301
17.6%
.24301
17.6%
123247
16.8%
913140
9.5%
27772
 
5.6%
67466
 
5.4%
77362
 
5.3%
46818
 
4.9%
56155
 
4.5%
36119
 
4.4%
Other values (2)11596
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number89675
64.9%
Other Punctuation48602
35.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123247
25.9%
913140
14.7%
27772
 
8.7%
67466
 
8.3%
77362
 
8.2%
46818
 
7.6%
56155
 
6.9%
36119
 
6.8%
85915
 
6.6%
05681
 
6.3%
Other Punctuation
ValueCountFrequency (%)
%24301
50.0%
.24301
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common138277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
%24301
17.6%
.24301
17.6%
123247
16.8%
913140
9.5%
27772
 
5.6%
67466
 
5.4%
77362
 
5.3%
46818
 
4.9%
56155
 
4.5%
36119
 
4.4%
Other values (2)11596
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII138277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
%24301
17.6%
.24301
17.6%
123247
16.8%
913140
9.5%
27772
 
5.6%
67466
 
5.4%
77362
 
5.3%
46818
 
4.9%
56155
 
4.5%
36119
 
4.4%
Other values (2)11596
8.4%

installment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11351
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.714343
Minimum16.08
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum16.08
5-th percentile73.26
Q1167.78
median280.97
Q3426.47
95-th percentile750.75
Maximum1305.19
Range1289.11
Interquartile range (IQR)258.69

Descriptive statistics

Standard deviation206.7294246
Coefficient of variation (CV)0.6386168208
Kurtosis1.287578775
Mean323.714343
Median Absolute Deviation (MAD)121.87
Skewness1.133111451
Sum7866582.25
Variance42737.055
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.1147
 
0.2%
180.9635
 
0.1%
311.0235
 
0.1%
186.6130
 
0.1%
339.3130
 
0.1%
187.6930
 
0.1%
312.8229
 
0.1%
303.2728
 
0.1%
150.827
 
0.1%
373.3327
 
0.1%
Other values (11341)23983
98.7%
ValueCountFrequency (%)
16.081
< 0.1%
16.471
< 0.1%
21.251
< 0.1%
22.941
< 0.1%
23.171
< 0.1%
23.511
< 0.1%
24.161
< 0.1%
24.271
< 0.1%
24.321
< 0.1%
24.731
< 0.1%
ValueCountFrequency (%)
1305.191
 
< 0.1%
1295.211
 
< 0.1%
1288.11
 
< 0.1%
1283.51
 
< 0.1%
1276.62
< 0.1%
1272.21
 
< 0.1%
1269.733
< 0.1%
1265.161
 
< 0.1%
1263.231
 
< 0.1%
1257.982
< 0.1%

grade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
B
7290 
A
6270 
C
4929 
D
3295 
E
1693 
Other values (2)
824 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24301
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowC
4th rowD
5th rowA

Common Values

ValueCountFrequency (%)
B7290
30.0%
A6270
25.8%
C4929
20.3%
D3295
13.6%
E1693
 
7.0%
F626
 
2.6%
G198
 
0.8%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
b7290
30.0%
a6270
25.8%
c4929
20.3%
d3295
13.6%
e1693
 
7.0%
f626
 
2.6%
g198
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B7290
30.0%
A6270
25.8%
C4929
20.3%
D3295
13.6%
E1693
 
7.0%
F626
 
2.6%
G198
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter24301
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B7290
30.0%
A6270
25.8%
C4929
20.3%
D3295
13.6%
E1693
 
7.0%
F626
 
2.6%
G198
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin24301
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B7290
30.0%
A6270
25.8%
C4929
20.3%
D3295
13.6%
E1693
 
7.0%
F626
 
2.6%
G198
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII24301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B7290
30.0%
A6270
25.8%
C4929
20.3%
D3295
13.6%
E1693
 
7.0%
F626
 
2.6%
G198
 
0.8%

sub_grade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
A4
1824 
A5
1741 
B3
1716 
B5
1677 
B4
 
1532
Other values (30)
15811 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters48602
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA4
2nd rowA5
3rd rowC3
4th rowD5
5th rowA5

Common Values

ValueCountFrequency (%)
A41824
 
7.5%
A51741
 
7.2%
B31716
 
7.1%
B51677
 
6.9%
B41532
 
6.3%
C11290
 
5.3%
B21259
 
5.2%
C21217
 
5.0%
A31107
 
4.6%
B11106
 
4.6%
Other values (25)9832
40.5%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
a41824
 
7.5%
a51741
 
7.2%
b31716
 
7.1%
b51677
 
6.9%
b41532
 
6.3%
c11290
 
5.3%
b21259
 
5.2%
c21217
 
5.0%
a31107
 
4.6%
b11106
 
4.6%
Other values (25)9832
40.5%

Most occurring characters

ValueCountFrequency (%)
B7290
15.0%
A6270
12.9%
45099
10.5%
55036
10.4%
34939
10.2%
C4929
10.1%
24818
9.9%
14409
9.1%
D3295
6.8%
E1693
 
3.5%
Other values (2)824
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter24301
50.0%
Decimal Number24301
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B7290
30.0%
A6270
25.8%
C4929
20.3%
D3295
13.6%
E1693
 
7.0%
F626
 
2.6%
G198
 
0.8%
Decimal Number
ValueCountFrequency (%)
45099
21.0%
55036
20.7%
34939
20.3%
24818
19.8%
14409
18.1%

Most occurring scripts

ValueCountFrequency (%)
Latin24301
50.0%
Common24301
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B7290
30.0%
A6270
25.8%
C4929
20.3%
D3295
13.6%
E1693
 
7.0%
F626
 
2.6%
G198
 
0.8%
Common
ValueCountFrequency (%)
45099
21.0%
55036
20.7%
34939
20.3%
24818
19.8%
14409
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII48602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B7290
15.0%
A6270
12.9%
45099
10.5%
55036
10.4%
34939
10.2%
C4929
10.1%
24818
9.9%
14409
9.1%
D3295
6.8%
E1693
 
3.5%
Other values (2)824
 
1.7%

emp_title
Categorical

HIGH CARDINALITY

Distinct19414
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
Bank of America
 
107
IBM
 
65
AT&T
 
57
Kaiser Permanente
 
56
Lockheed Martin
 
42
Other values (19409)
23974 

Length

Max length78
Median length18
Mean length18.47672935
Min length0

Characters and Unicode

Total characters449003
Distinct characters92
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17319 ?
Unique (%)71.3%

Sample

1st row15
2nd row1400
3rd row36000
4th row old palm inc
5th row Brocade Communications

Common Values

ValueCountFrequency (%)
Bank of America107
 
0.4%
IBM65
 
0.3%
AT&T57
 
0.2%
Kaiser Permanente56
 
0.2%
Lockheed Martin42
 
0.2%
JPMorgan Chase35
 
0.1%
JP Morgan Chase35
 
0.1%
Department of Defense35
 
0.1%
Booz Allen Hamilton34
 
0.1%
Northrop Grumman34
 
0.1%
Other values (19404)23801
97.9%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
inc2207
 
3.4%
of1891
 
2.9%
851
 
1.3%
and677
 
1.0%
city589
 
0.9%
county588
 
0.9%
services550
 
0.8%
bank543
 
0.8%
center534
 
0.8%
school475
 
0.7%
Other values (14017)56550
86.4%

Most occurring characters

ValueCountFrequency (%)
42144
 
9.4%
e36072
 
8.0%
a29265
 
6.5%
n29008
 
6.5%
o28301
 
6.3%
i26371
 
5.9%
r26066
 
5.8%
t24720
 
5.5%
s19451
 
4.3%
l17413
 
3.9%
Other values (82)170192
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter321540
71.6%
Uppercase Letter77978
 
17.4%
Space Separator42144
 
9.4%
Other Punctuation5770
 
1.3%
Decimal Number732
 
0.2%
Dash Punctuation630
 
0.1%
Open Punctuation94
 
< 0.1%
Close Punctuation93
 
< 0.1%
Math Symbol14
 
< 0.1%
Other Symbol2
 
< 0.1%
Other values (4)6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C10745
13.8%
A6371
 
8.2%
S5874
 
7.5%
I5416
 
6.9%
M4936
 
6.3%
L4144
 
5.3%
P4085
 
5.2%
B3858
 
4.9%
E3831
 
4.9%
D3796
 
4.9%
Other values (18)24922
32.0%
Lowercase Letter
ValueCountFrequency (%)
e36072
11.2%
a29265
9.1%
n29008
9.0%
o28301
8.8%
i26371
 
8.2%
r26066
 
8.1%
t24720
 
7.7%
s19451
 
6.0%
l17413
 
5.4%
c15709
 
4.9%
Other values (17)69164
21.5%
Other Punctuation
ValueCountFrequency (%)
.2594
45.0%
,1504
26.1%
&965
 
16.7%
'442
 
7.7%
/205
 
3.6%
#25
 
0.4%
@7
 
0.1%
"7
 
0.1%
!5
 
0.1%
:5
 
0.1%
Other values (4)11
 
0.2%
Decimal Number
ValueCountFrequency (%)
1152
20.8%
2128
17.5%
3115
15.7%
073
10.0%
470
9.6%
553
 
7.2%
644
 
6.0%
943
 
5.9%
731
 
4.2%
823
 
3.1%
Open Punctuation
ValueCountFrequency (%)
(93
98.9%
[1
 
1.1%
Math Symbol
ValueCountFrequency (%)
+13
92.9%
|1
 
7.1%
Currency Symbol
ValueCountFrequency (%)
¢1
50.0%
$1
50.0%
Space Separator
ValueCountFrequency (%)
42144
100.0%
Dash Punctuation
ValueCountFrequency (%)
-630
100.0%
Close Punctuation
ValueCountFrequency (%)
)93
100.0%
Other Symbol
ValueCountFrequency (%)
©2
100.0%
Other Number
ValueCountFrequency (%)
²2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%
Control
ValueCountFrequency (%)
€1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin399518
89.0%
Common49485
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e36072
 
9.0%
a29265
 
7.3%
n29008
 
7.3%
o28301
 
7.1%
i26371
 
6.6%
r26066
 
6.5%
t24720
 
6.2%
s19451
 
4.9%
l17413
 
4.4%
c15709
 
3.9%
Other values (45)147142
36.8%
Common
ValueCountFrequency (%)
42144
85.2%
.2594
 
5.2%
,1504
 
3.0%
&965
 
2.0%
-630
 
1.3%
'442
 
0.9%
/205
 
0.4%
1152
 
0.3%
2128
 
0.3%
3115
 
0.2%
Other values (27)606
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII448993
> 99.9%
None10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42144
 
9.4%
e36072
 
8.0%
a29265
 
6.5%
n29008
 
6.5%
o28301
 
6.3%
i26371
 
5.9%
r26066
 
5.8%
t24720
 
5.5%
s19451
 
4.3%
l17413
 
3.9%
Other values (75)170182
37.9%
None
ValueCountFrequency (%)
Ã2
20.0%
©2
20.0%
²2
20.0%
Â1
10.0%
â1
10.0%
€1
10.0%
¢1
10.0%

emp_length
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.021192544
Minimum0
Maximum10
Zeros40
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.381479975
Coefficient of variation (CV)0.673441607
Kurtosis-1.37860354
Mean5.021192544
Median Absolute Deviation (MAD)3
Skewness0.3377068344
Sum122020
Variance11.43440682
MonotonicityNot monotonic
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
105315
21.9%
14897
20.2%
22806
11.5%
32675
11.0%
42223
9.1%
52087
 
8.6%
61385
 
5.7%
71108
 
4.6%
8942
 
3.9%
9823
 
3.4%
ValueCountFrequency (%)
040
 
0.2%
14897
20.2%
22806
11.5%
32675
11.0%
42223
9.1%
52087
8.6%
61385
 
5.7%
71108
 
4.6%
8942
 
3.9%
9823
 
3.4%
ValueCountFrequency (%)
105315
21.9%
9823
 
3.4%
8942
 
3.9%
71108
 
4.6%
61385
 
5.7%
52087
 
8.6%
42223
9.1%
32675
11.0%
22806
11.5%
14897
20.2%

home_ownership
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
RENT
11777 
MORTGAGE
10726 
OWN
1736 
OTHER
 
62

Length

Max length8
Median length4
Mean length5.696637998
Min length3

Characters and Unicode

Total characters138434
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowOWN
4th rowMORTGAGE
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
RENT11777
48.5%
MORTGAGE10726
44.1%
OWN1736
 
7.1%
OTHER62
 
0.3%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
rent11777
48.5%
mortgage10726
44.1%
own1736
 
7.1%
other62
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T22565
16.3%
E22565
16.3%
R22565
16.3%
G21452
15.5%
N13513
9.8%
O12524
9.0%
A10726
7.7%
M10726
7.7%
W1736
 
1.3%
H62
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter138434
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T22565
16.3%
E22565
16.3%
R22565
16.3%
G21452
15.5%
N13513
9.8%
O12524
9.0%
A10726
7.7%
M10726
7.7%
W1736
 
1.3%
H62
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin138434
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T22565
16.3%
E22565
16.3%
R22565
16.3%
G21452
15.5%
N13513
9.8%
O12524
9.0%
A10726
7.7%
M10726
7.7%
W1736
 
1.3%
H62
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII138434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T22565
16.3%
E22565
16.3%
R22565
16.3%
G21452
15.5%
N13513
9.8%
O12524
9.0%
A10726
7.7%
M10726
7.7%
W1736
 
1.3%
H62
 
< 0.1%

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct3557
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69802.25015
Minimum4080
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum4080
5-th percentile24947.3
Q142000
median60000
Q384000
95-th percentile140900
Maximum6000000
Range5995920
Interquartile range (IQR)42000

Descriptive statistics

Standard deviation69999.15248
Coefficient of variation (CV)1.002820859
Kurtosis2575.276323
Mean69802.25015
Median Absolute Deviation (MAD)20000
Skewness36.43261349
Sum1696264481
Variance4899881348
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000902
 
3.7%
50000662
 
2.7%
45000546
 
2.2%
40000528
 
2.2%
75000504
 
2.1%
65000501
 
2.1%
30000484
 
2.0%
70000470
 
1.9%
48000437
 
1.8%
80000421
 
1.7%
Other values (3547)18846
77.6%
ValueCountFrequency (%)
40801
 
< 0.1%
42001
 
< 0.1%
48001
 
< 0.1%
55001
 
< 0.1%
60003
 
< 0.1%
72002
 
< 0.1%
80003
 
< 0.1%
80041
 
< 0.1%
84721
 
< 0.1%
960015
0.1%
ValueCountFrequency (%)
60000001
< 0.1%
39000001
< 0.1%
20397841
< 0.1%
19000001
< 0.1%
17820001
< 0.1%
14400001
< 0.1%
13620001
< 0.1%
12000002
< 0.1%
11760001
< 0.1%
9480001
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
Not Verified
10614 
Verified
7525 
Source Verified
6162 

Length

Max length15
Median length12
Mean length11.52207728
Min length8

Characters and Unicode

Total characters279998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSource Verified
2nd rowNot Verified
3rd rowNot Verified
4th rowNot Verified
5th rowNot Verified

Common Values

ValueCountFrequency (%)
Not Verified10614
43.7%
Verified7525
31.0%
Source Verified6162
25.4%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
verified24301
59.2%
not10614
25.8%
source6162
 
15.0%

Most occurring characters

ValueCountFrequency (%)
e54764
19.6%
i48602
17.4%
r30463
10.9%
d24301
8.7%
f24301
8.7%
V24301
8.7%
16776
 
6.0%
o16776
 
6.0%
t10614
 
3.8%
N10614
 
3.8%
Other values (3)18486
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter222145
79.3%
Uppercase Letter41077
 
14.7%
Space Separator16776
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e54764
24.7%
i48602
21.9%
r30463
13.7%
d24301
10.9%
f24301
10.9%
o16776
 
7.6%
t10614
 
4.8%
c6162
 
2.8%
u6162
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V24301
59.2%
N10614
25.8%
S6162
 
15.0%
Space Separator
ValueCountFrequency (%)
16776
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin263222
94.0%
Common16776
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e54764
20.8%
i48602
18.5%
r30463
11.6%
d24301
9.2%
f24301
9.2%
V24301
9.2%
o16776
 
6.4%
t10614
 
4.0%
N10614
 
4.0%
c6162
 
2.3%
Other values (2)12324
 
4.7%
Common
ValueCountFrequency (%)
16776
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII279998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e54764
19.6%
i48602
17.4%
r30463
10.9%
d24301
8.7%
f24301
8.7%
V24301
8.7%
16776
 
6.0%
o16776
 
6.0%
t10614
 
3.8%
N10614
 
3.8%
Other values (3)18486
 
6.6%

issue_d
Categorical

HIGH CARDINALITY

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
11-Dec
 
1280
11-Nov
 
1258
11-Sep
 
1234
11-Oct
 
1184
11-Aug
 
1141
Other values (50)
18204 

Length

Max length6
Median length6
Mean length5.831323814
Min length5

Characters and Unicode

Total characters141707
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10-Sep
2nd row10-Jan
3rd row9-Oct
4th row11-Jul
5th row9-Dec

Common Values

ValueCountFrequency (%)
11-Dec1280
 
5.3%
11-Nov1258
 
5.2%
11-Sep1234
 
5.1%
11-Oct1184
 
4.9%
11-Aug1141
 
4.7%
11-Jul1098
 
4.5%
11-Jun1089
 
4.5%
11-Apr1005
 
4.1%
11-May1003
 
4.1%
11-Mar931
 
3.8%
Other values (45)13078
53.8%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
11-dec1280
 
5.3%
11-nov1258
 
5.2%
11-sep1234
 
5.1%
11-oct1184
 
4.9%
11-aug1141
 
4.7%
11-jul1098
 
4.5%
11-jun1089
 
4.5%
11-apr1005
 
4.1%
11-may1003
 
4.1%
11-mar931
 
3.8%
Other values (45)13078
53.8%

Most occurring characters

ValueCountFrequency (%)
133112
23.4%
-24301
17.1%
07292
 
5.1%
u6314
 
4.5%
e6313
 
4.5%
J5636
 
4.0%
a5063
 
3.6%
c4957
 
3.5%
p4015
 
2.8%
A3963
 
2.8%
Other values (18)40741
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter48602
34.3%
Decimal Number44503
31.4%
Dash Punctuation24301
17.1%
Uppercase Letter24301
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u6314
13.0%
e6313
13.0%
a5063
10.4%
c4957
10.2%
p4015
8.3%
r3528
7.3%
n3509
7.2%
v2495
 
5.1%
o2495
 
5.1%
t2330
 
4.8%
Other values (4)7583
15.6%
Uppercase Letter
ValueCountFrequency (%)
J5636
23.2%
A3963
16.3%
M3564
14.7%
D2627
10.8%
N2495
10.3%
O2330
9.6%
S2229
 
9.2%
F1457
 
6.0%
Decimal Number
ValueCountFrequency (%)
133112
74.4%
07292
 
16.4%
92996
 
6.7%
8953
 
2.1%
7150
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
-24301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72903
51.4%
Common68804
48.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
u6314
 
8.7%
e6313
 
8.7%
J5636
 
7.7%
a5063
 
6.9%
c4957
 
6.8%
p4015
 
5.5%
A3963
 
5.4%
M3564
 
4.9%
r3528
 
4.8%
n3509
 
4.8%
Other values (12)26041
35.7%
Common
ValueCountFrequency (%)
133112
48.1%
-24301
35.3%
07292
 
10.6%
92996
 
4.4%
8953
 
1.4%
7150
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII141707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133112
23.4%
-24301
17.1%
07292
 
5.1%
u6314
 
4.5%
e6313
 
4.5%
J5636
 
4.0%
a5063
 
3.6%
c4957
 
3.5%
p4015
 
2.8%
A3963
 
2.8%
Other values (18)40741
28.8%

loan_status
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
1
20827 
0
3474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24301
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
120827
85.7%
03474
 
14.3%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
120827
85.7%
03474
 
14.3%

Most occurring characters

ValueCountFrequency (%)
120827
85.7%
03474
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24301
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120827
85.7%
03474
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common24301
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
120827
85.7%
03474
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120827
85.7%
03474
 
14.3%

desc
Categorical

HIGH CARDINALITY

Distinct16214
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
7943 
 
135
Debt Consolidation
 
4
debt consolidation
 
2
Camping Membership
 
2
Other values (16209)
16215 

Length

Max length3976
Median length143
Mean length288.8925559
Min length0

Characters and Unicode

Total characters7020378
Distinct characters139
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16203 ?
Unique (%)66.7%

Sample

1st row Borrower added on 08/30/10 > thank you<br/>
2nd row Borrower added on 01/14/10 > Green city housing project<br/> Borrower added on 01/23/10 > I mistyped in the application. $1400 is the current rent we pay. <br/><br/>My employer is Emerson, and my role is marketing and biz dev director. Been with them for 3 years now. My 2009 annual compensation was ~160K. <br/><br/>I have zero credit card debt or car loans. Purpose of the loan is to buy real estate in my home town in India. It costs ~$140K. Rather than taking a mortgage there, I want to buy the property outright. I used $125K from my savings, and I am short $15K. The name of the building community is Green City - www.vplprojects.com.<br/>
3rd row
4th row Borrower added on 06/29/11 > thanks for the help.<br/>
5th row

Common Values

ValueCountFrequency (%)
7943
32.7%
135
 
0.6%
Debt Consolidation4
 
< 0.1%
debt consolidation2
 
< 0.1%
Camping Membership2
 
< 0.1%
refinancing2
 
< 0.1%
Borrower added on 09/21/11 > Debt consolidation<br/>2
 
< 0.1%
credit card debt consolidation2
 
< 0.1%
Motorcycle Loan2
 
< 0.1%
I have 2nd mortgage on a rental property with balance of $109k. I have cash flow of $85k and plan to pay off the 2nd mortgage using this loan to make up for rest of the balance. You will see a 2nd mortgage debt on my credit around $965.00 which will go away once this loan is approved as I will be paying it off. The interest rate needs to be below 9% since I can use my credit card to borrow same money for 10%.2
 
< 0.1%
Other values (16204)16205
66.7%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
i48123
 
3.8%
to44000
 
3.5%
a33899
 
2.7%
the33491
 
2.7%
and33449
 
2.6%
my31874
 
2.5%
on29722
 
2.4%
22434
 
1.8%
for20249
 
1.6%
have20079
 
1.6%
Other values (38785)945839
74.9%

Most occurring characters

ValueCountFrequency (%)
1304898
18.6%
e586334
 
8.4%
a440234
 
6.3%
o435950
 
6.2%
t401109
 
5.7%
n376628
 
5.4%
r360963
 
5.1%
i305383
 
4.3%
s261770
 
3.7%
d244500
 
3.5%
Other values (129)2302609
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5008568
71.3%
Space Separator1304939
 
18.6%
Decimal Number210987
 
3.0%
Other Punctuation199437
 
2.8%
Uppercase Letter182166
 
2.6%
Math Symbol85239
 
1.2%
Currency Symbol10266
 
0.1%
Dash Punctuation8299
 
0.1%
Close Punctuation4580
 
0.1%
Open Punctuation4182
 
0.1%
Other values (7)1715
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I58410
32.1%
B20955
 
11.5%
T17199
 
9.4%
A9232
 
5.1%
M8661
 
4.8%
C8605
 
4.7%
S5533
 
3.0%
W5308
 
2.9%
L5254
 
2.9%
E5253
 
2.9%
Other values (21)37756
20.7%
Lowercase Letter
ValueCountFrequency (%)
e586334
11.7%
a440234
 
8.8%
o435950
 
8.7%
t401109
 
8.0%
n376628
 
7.5%
r360963
 
7.2%
i305383
 
6.1%
s261770
 
5.2%
d244500
 
4.9%
l219169
 
4.4%
Other values (17)1376528
27.5%
Other Punctuation
ValueCountFrequency (%)
.73931
37.1%
/70589
35.4%
,30508
15.3%
'8217
 
4.1%
!4228
 
2.1%
%3477
 
1.7%
:3306
 
1.7%
;2056
 
1.0%
&1605
 
0.8%
"504
 
0.3%
Other values (10)1016
 
0.5%
Control
ValueCountFrequency (%)
770
54.3%
€321
22.6%
™147
 
10.4%
‚31
 
2.2%
“29
 
2.0%
ƒ25
 
1.8%
’25
 
1.8%
21
 
1.5%
œ18
 
1.3%
š13
 
0.9%
Other values (7)19
 
1.3%
Decimal Number
ValueCountFrequency (%)
063678
30.2%
158777
27.9%
222312
 
10.6%
513234
 
6.3%
311014
 
5.2%
910049
 
4.8%
48272
 
3.9%
67927
 
3.8%
77866
 
3.7%
87858
 
3.7%
Math Symbol
ValueCountFrequency (%)
>51369
60.3%
<32674
38.3%
+624
 
0.7%
=350
 
0.4%
~192
 
0.2%
¬25
 
< 0.1%
|5
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
¦95
88.8%
©9
 
8.4%
®2
 
1.9%
1
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-8286
99.8%
9
 
0.1%
4
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)4559
99.5%
]18
 
0.4%
}3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(4163
99.5%
[17
 
0.4%
{2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
`8
66.7%
^3
 
25.0%
¯1
 
8.3%
Space Separator
ValueCountFrequency (%)
1304898
> 99.9%
 41
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$10220
99.6%
¢46
 
0.4%
Final Punctuation
ValueCountFrequency (%)
53
84.1%
10
 
15.9%
Initial Punctuation
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
Other Number
ValueCountFrequency (%)
½5
71.4%
¾2
 
28.6%
Connector Punctuation
ValueCountFrequency (%)
_96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5190734
73.9%
Common1829644
 
26.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1304898
71.3%
.73931
 
4.0%
/70589
 
3.9%
063678
 
3.5%
158777
 
3.2%
>51369
 
2.8%
<32674
 
1.8%
,30508
 
1.7%
222312
 
1.2%
513234
 
0.7%
Other values (71)107674
 
5.9%
Latin
ValueCountFrequency (%)
e586334
 
11.3%
a440234
 
8.5%
o435950
 
8.4%
t401109
 
7.7%
n376628
 
7.3%
r360963
 
7.0%
i305383
 
5.9%
s261770
 
5.0%
d244500
 
4.7%
l219169
 
4.2%
Other values (48)1558694
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7018860
> 99.9%
None1414
 
< 0.1%
Punctuation103
 
< 0.1%
Specials1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1304898
18.6%
e586334
 
8.4%
a440234
 
6.3%
o435950
 
6.2%
t401109
 
5.7%
n376628
 
5.4%
r360963
 
5.1%
i305383
 
4.4%
s261770
 
3.7%
d244500
 
3.5%
Other values (86)2301091
32.8%
None
ValueCountFrequency (%)
â342
24.2%
€321
22.7%
™147
10.4%
¦95
 
6.7%
Â87
 
6.2%
Ã81
 
5.7%
¢46
 
3.3%
 41
 
2.9%
‚31
 
2.2%
“29
 
2.1%
Other values (24)194
13.7%
Punctuation
ValueCountFrequency (%)
53
51.5%
10
 
9.7%
10
 
9.7%
9
 
8.7%
8
 
7.8%
8
 
7.8%
4
 
3.9%
1
 
1.0%
Specials
ValueCountFrequency (%)
1
100.0%

purpose
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
debt_consolidation
11572 
credit_card
3178 
other
2397 
home_improvement
1826 
major_purchase
1348 
Other values (9)
3980 

Length

Max length18
Median length16
Mean length13.77350726
Min length3

Characters and Unicode

Total characters334710
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhome_improvement
2nd rowother
3rd rowmajor_purchase
4th rowdebt_consolidation
5th rowother

Common Values

ValueCountFrequency (%)
debt_consolidation11572
47.6%
credit_card3178
 
13.1%
other2397
 
9.9%
home_improvement1826
 
7.5%
major_purchase1348
 
5.5%
car957
 
3.9%
small_business930
 
3.8%
wedding600
 
2.5%
medical429
 
1.8%
moving344
 
1.4%
Other values (4)720
 
3.0%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation11572
47.6%
credit_card3178
 
13.1%
other2397
 
9.9%
home_improvement1826
 
7.5%
major_purchase1348
 
5.5%
car957
 
3.9%
small_business930
 
3.8%
wedding600
 
2.5%
medical429
 
1.8%
moving344
 
1.4%
Other values (4)720
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o43117
12.9%
d31340
9.4%
t30989
9.3%
i30895
9.2%
n27408
8.2%
e26659
 
8.0%
c21106
 
6.3%
a20710
 
6.2%
_18914
 
5.7%
s16856
 
5.0%
Other values (12)66716
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315796
94.3%
Connector Punctuation18914
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o43117
13.7%
d31340
9.9%
t30989
9.8%
i30895
9.8%
n27408
8.7%
e26659
8.4%
c21106
6.7%
a20710
 
6.6%
s16856
 
5.3%
r14352
 
4.5%
Other values (11)52364
16.6%
Connector Punctuation
ValueCountFrequency (%)
_18914
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin315796
94.3%
Common18914
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o43117
13.7%
d31340
9.9%
t30989
9.8%
i30895
9.8%
n27408
8.7%
e26659
8.4%
c21106
6.7%
a20710
 
6.6%
s16856
 
5.3%
r14352
 
4.5%
Other values (11)52364
16.6%
Common
ValueCountFrequency (%)
_18914
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII334710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o43117
12.9%
d31340
9.4%
t30989
9.3%
i30895
9.2%
n27408
8.2%
e26659
 
8.0%
c21106
 
6.3%
a20710
 
6.2%
_18914
 
5.7%
s16856
 
5.0%
Other values (12)66716
19.9%

title
Categorical

HIGH CARDINALITY

Distinct12699
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
Debt Consolidation
 
1351
Debt Consolidation Loan
 
1022
Personal Loan
 
429
Consolidation
 
325
debt consolidation
 
295
Other values (12694)
20879 

Length

Max length80
Median length16
Mean length17.29015267
Min length0

Characters and Unicode

Total characters420168
Distinct characters100
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11432 ?
Unique (%)47.0%

Sample

1st rowhomeimproement
2nd rowGreen City
3rd rowpayoff w/in 2 years
4th rowsave %
5th rowInvest in Lending Club Notes

Common Values

ValueCountFrequency (%)
Debt Consolidation1351
 
5.6%
Debt Consolidation Loan1022
 
4.2%
Personal Loan429
 
1.8%
Consolidation325
 
1.3%
debt consolidation295
 
1.2%
Home Improvement225
 
0.9%
Debt consolidation219
 
0.9%
Credit Card Consolidation219
 
0.9%
Personal188
 
0.8%
Credit Card Loan185
 
0.8%
Other values (12689)19843
81.7%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
loan6654
 
10.3%
debt5702
 
8.9%
consolidation5326
 
8.3%
credit2893
 
4.5%
card2095
 
3.3%
personal1315
 
2.0%
home1156
 
1.8%
pay848
 
1.3%
off798
 
1.2%
my721
 
1.1%
Other values (6390)36833
57.2%

Most occurring characters

ValueCountFrequency (%)
40788
 
9.7%
o40574
 
9.7%
n34317
 
8.2%
e33449
 
8.0%
a31034
 
7.4%
i27068
 
6.4%
t26268
 
6.3%
d19098
 
4.5%
r17960
 
4.3%
s17318
 
4.1%
Other values (90)132294
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter320978
76.4%
Uppercase Letter50832
 
12.1%
Space Separator40788
 
9.7%
Decimal Number3873
 
0.9%
Other Punctuation2817
 
0.7%
Dash Punctuation504
 
0.1%
Connector Punctuation123
 
< 0.1%
Close Punctuation66
 
< 0.1%
Currency Symbol64
 
< 0.1%
Math Symbol58
 
< 0.1%
Other values (5)65
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o40574
12.6%
n34317
10.7%
e33449
10.4%
a31034
9.7%
i27068
8.4%
t26268
8.2%
d19098
 
5.9%
r17960
 
5.6%
s17318
 
5.4%
l16104
 
5.0%
Other values (17)57788
18.0%
Uppercase Letter
ValueCountFrequency (%)
C11472
22.6%
L6291
12.4%
D5679
11.2%
P3503
 
6.9%
R2347
 
4.6%
M1921
 
3.8%
S1859
 
3.7%
B1827
 
3.6%
E1760
 
3.5%
H1742
 
3.4%
Other values (17)12431
24.5%
Other Punctuation
ValueCountFrequency (%)
!700
24.8%
'637
22.6%
.448
15.9%
/351
12.5%
,273
 
9.7%
&202
 
7.2%
%56
 
2.0%
:38
 
1.3%
"35
 
1.2%
*22
 
0.8%
Other values (5)55
 
2.0%
Decimal Number
ValueCountFrequency (%)
11101
28.4%
01092
28.2%
2696
18.0%
3197
 
5.1%
5169
 
4.4%
9164
 
4.2%
4150
 
3.9%
6111
 
2.9%
8104
 
2.7%
789
 
2.3%
Math Symbol
ValueCountFrequency (%)
+30
51.7%
=12
 
20.7%
<9
 
15.5%
>6
 
10.3%
~1
 
1.7%
Control
ValueCountFrequency (%)
€4
44.4%
2
22.2%
™2
22.2%
–1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
)64
97.0%
]2
 
3.0%
Open Punctuation
ValueCountFrequency (%)
(49
96.1%
[2
 
3.9%
Modifier Symbol
ValueCountFrequency (%)
`1
50.0%
^1
50.0%
Space Separator
ValueCountFrequency (%)
40788
100.0%
Dash Punctuation
ValueCountFrequency (%)
-504
100.0%
Connector Punctuation
ValueCountFrequency (%)
_123
100.0%
Currency Symbol
ValueCountFrequency (%)
$64
100.0%
Other Symbol
ValueCountFrequency (%)
¦2
100.0%
Other Number
ValueCountFrequency (%)
³1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin371810
88.5%
Common48358
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o40574
 
10.9%
n34317
 
9.2%
e33449
 
9.0%
a31034
 
8.3%
i27068
 
7.3%
t26268
 
7.1%
d19098
 
5.1%
r17960
 
4.8%
s17318
 
4.7%
l16104
 
4.3%
Other values (44)108620
29.2%
Common
ValueCountFrequency (%)
40788
84.3%
11101
 
2.3%
01092
 
2.3%
!700
 
1.4%
2696
 
1.4%
'637
 
1.3%
-504
 
1.0%
.448
 
0.9%
/351
 
0.7%
,273
 
0.6%
Other values (36)1768
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII420153
> 99.9%
None15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40788
 
9.7%
o40574
 
9.7%
n34317
 
8.2%
e33449
 
8.0%
a31034
 
7.4%
i27068
 
6.4%
t26268
 
6.3%
d19098
 
4.5%
r17960
 
4.3%
s17318
 
4.1%
Other values (83)132279
31.5%
None
ValueCountFrequency (%)
€4
26.7%
â4
26.7%
¦2
13.3%
™2
13.3%
–1
 
6.7%
Ã1
 
6.7%
³1
 
6.7%

zip_code
Categorical

HIGH CARDINALITY

Distinct783
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
100xx
 
399
112xx
 
371
945xx
 
344
606xx
 
339
070xx
 
302
Other values (778)
22546 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters121505
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)0.3%

Sample

1st row076xx
2nd row300xx
3rd row310xx
4th row334xx
5th row939xx

Common Values

ValueCountFrequency (%)
100xx399
 
1.6%
112xx371
 
1.5%
945xx344
 
1.4%
606xx339
 
1.4%
070xx302
 
1.2%
900xx291
 
1.2%
021xx253
 
1.0%
750xx247
 
1.0%
300xx246
 
1.0%
331xx243
 
1.0%
Other values (773)21266
87.5%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
100xx399
 
1.6%
112xx371
 
1.5%
945xx344
 
1.4%
606xx339
 
1.4%
070xx302
 
1.2%
900xx291
 
1.2%
021xx253
 
1.0%
750xx247
 
1.0%
300xx246
 
1.0%
331xx243
 
1.0%
Other values (773)21266
87.5%

Most occurring characters

ValueCountFrequency (%)
x48602
40.0%
012402
 
10.2%
19998
 
8.2%
28335
 
6.9%
97533
 
6.2%
37520
 
6.2%
76263
 
5.2%
45589
 
4.6%
55305
 
4.4%
85178
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72903
60.0%
Lowercase Letter48602
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012402
17.0%
19998
13.7%
28335
11.4%
97533
10.3%
37520
10.3%
76263
8.6%
45589
7.7%
55305
7.3%
85178
7.1%
64780
 
6.6%
Lowercase Letter
ValueCountFrequency (%)
x48602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common72903
60.0%
Latin48602
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012402
17.0%
19998
13.7%
28335
11.4%
97533
10.3%
37520
10.3%
76263
8.6%
45589
7.7%
55305
7.3%
85178
7.1%
64780
 
6.6%
Latin
ValueCountFrequency (%)
x48602
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII121505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x48602
40.0%
012402
 
10.2%
19998
 
8.2%
28335
 
6.9%
97533
 
6.2%
37520
 
6.2%
76263
 
5.2%
45589
 
4.6%
55305
 
4.4%
85178
 
4.3%

addr_state
Categorical

Distinct50
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
CA
4337 
NY
2537 
FL
1750 
TX
1654 
NJ
 
1179
Other values (45)
12844 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters48602
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNJ
2nd rowGA
3rd rowGA
4th rowFL
5th rowCA

Common Values

ValueCountFrequency (%)
CA4337
17.8%
NY2537
 
10.4%
FL1750
 
7.2%
TX1654
 
6.8%
NJ1179
 
4.9%
IL1006
 
4.1%
PA951
 
3.9%
GA840
 
3.5%
MA827
 
3.4%
VA826
 
3.4%
Other values (40)8394
34.5%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
ca4337
17.8%
ny2537
 
10.4%
fl1750
 
7.2%
tx1654
 
6.8%
nj1179
 
4.9%
il1006
 
4.1%
pa951
 
3.9%
ga840
 
3.5%
ma827
 
3.4%
va826
 
3.4%
Other values (40)8394
34.5%

Most occurring characters

ValueCountFrequency (%)
A9480
19.5%
C6101
12.6%
N5071
10.4%
L3281
 
6.8%
M2887
 
5.9%
Y2778
 
5.7%
T2297
 
4.7%
O2074
 
4.3%
I1962
 
4.0%
F1750
 
3.6%
Other values (14)10921
22.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter48602
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A9480
19.5%
C6101
12.6%
N5071
10.4%
L3281
 
6.8%
M2887
 
5.9%
Y2778
 
5.7%
T2297
 
4.7%
O2074
 
4.3%
I1962
 
4.0%
F1750
 
3.6%
Other values (14)10921
22.5%

Most occurring scripts

ValueCountFrequency (%)
Latin48602
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A9480
19.5%
C6101
12.6%
N5071
10.4%
L3281
 
6.8%
M2887
 
5.9%
Y2778
 
5.7%
T2297
 
4.7%
O2074
 
4.3%
I1962
 
4.0%
F1750
 
3.6%
Other values (14)10921
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII48602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A9480
19.5%
C6101
12.6%
N5071
10.4%
L3281
 
6.8%
M2887
 
5.9%
Y2778
 
5.7%
T2297
 
4.7%
O2074
 
4.3%
I1962
 
4.0%
F1750
 
3.6%
Other values (14)10921
22.5%

dti
Real number (ℝ≥0)

Distinct2767
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.29391136
Minimum0
Maximum29.99
Zeros112
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile2.16
Q18.18
median13.39
Q318.53
95-th percentile23.78
Maximum29.99
Range29.99
Interquartile range (IQR)10.35

Descriptive statistics

Standard deviation6.646290476
Coefficient of variation (CV)0.4999499617
Kurtosis-0.8495096786
Mean13.29391136
Median Absolute Deviation (MAD)5.17
Skewness-0.02981093437
Sum323055.34
Variance44.17317709
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0112
 
0.5%
12.4829
 
0.1%
1828
 
0.1%
1227
 
0.1%
19.227
 
0.1%
17.0426
 
0.1%
9.625
 
0.1%
8.422
 
0.1%
13.522
 
0.1%
11.522
 
0.1%
Other values (2757)23961
98.6%
ValueCountFrequency (%)
0112
0.5%
0.012
 
< 0.1%
0.023
 
< 0.1%
0.043
 
< 0.1%
0.051
 
< 0.1%
0.072
 
< 0.1%
0.082
 
< 0.1%
0.092
 
< 0.1%
0.121
 
< 0.1%
0.136
 
< 0.1%
ValueCountFrequency (%)
29.991
< 0.1%
29.931
< 0.1%
29.921
< 0.1%
29.861
< 0.1%
29.851
< 0.1%
29.821
< 0.1%
29.791
< 0.1%
29.781
< 0.1%
29.761
< 0.1%
29.732
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1454261142
Minimum0
Maximum11
Zeros21691
Zeros (%)89.3%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4948318962
Coefficient of variation (CV)3.402634383
Kurtosis46.15256725
Mean0.1454261142
Median Absolute Deviation (MAD)0
Skewness5.308820701
Sum3534
Variance0.2448586055
MonotonicityNot monotonic
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
021691
89.3%
12000
 
8.2%
2411
 
1.7%
3137
 
0.6%
437
 
0.2%
513
 
0.1%
74
 
< 0.1%
64
 
< 0.1%
82
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
021691
89.3%
12000
 
8.2%
2411
 
1.7%
3137
 
0.6%
437
 
0.2%
513
 
0.1%
64
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
74
 
< 0.1%
64
 
< 0.1%
513
 
0.1%
437
 
0.2%
3137
 
0.6%
2411
 
1.7%
12000
8.2%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct495
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
Nov-98
 
239
Oct-99
 
230
Dec-98
 
227
Oct-00
 
207
Nov-99
 
205
Other values (490)
23193 

Length

Max length6
Median length6
Mean length5.701699519
Min length5

Characters and Unicode

Total characters138557
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)0.2%

Sample

1st row1-Feb
2nd rowFeb-97
3rd rowMar-00
4th row4-Jun
5th rowAug-93

Common Values

ValueCountFrequency (%)
Nov-98239
 
1.0%
Oct-99230
 
0.9%
Dec-98227
 
0.9%
Oct-00207
 
0.9%
Nov-99205
 
0.8%
Nov-97203
 
0.8%
Dec-99203
 
0.8%
Nov-00201
 
0.8%
Sep-00196
 
0.8%
Dec-97191
 
0.8%
Other values (485)22199
91.4%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
nov-98239
 
1.0%
oct-99230
 
0.9%
dec-98227
 
0.9%
oct-00207
 
0.9%
nov-99205
 
0.8%
nov-97203
 
0.8%
dec-99203
 
0.8%
nov-00201
 
0.8%
sep-00196
 
0.8%
dec-97191
 
0.8%
Other values (485)22199
91.4%

Most occurring characters

ValueCountFrequency (%)
-24301
17.5%
914355
 
10.4%
e6440
 
4.6%
J5706
 
4.1%
u5693
 
4.1%
a5590
 
4.0%
85062
 
3.7%
c4978
 
3.6%
04797
 
3.5%
p3830
 
2.8%
Other values (23)57805
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter48602
35.1%
Decimal Number41353
29.8%
Dash Punctuation24301
17.5%
Uppercase Letter24301
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6440
13.3%
u5693
11.7%
a5590
11.5%
c4978
10.2%
p3830
7.9%
n3827
7.9%
r3335
6.9%
t2495
 
5.1%
v2454
 
5.0%
o2454
 
5.0%
Other values (4)7506
15.4%
Decimal Number
ValueCountFrequency (%)
914355
34.7%
85062
 
12.2%
04797
 
11.6%
72895
 
7.0%
42614
 
6.3%
52519
 
6.1%
62476
 
6.0%
12326
 
5.6%
32277
 
5.5%
22032
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
J5706
23.5%
A3699
15.2%
M3507
14.4%
O2495
10.3%
D2483
10.2%
N2454
10.1%
S2201
 
9.1%
F1756
 
7.2%
Dash Punctuation
ValueCountFrequency (%)
-24301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72903
52.6%
Common65654
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6440
 
8.8%
J5706
 
7.8%
u5693
 
7.8%
a5590
 
7.7%
c4978
 
6.8%
p3830
 
5.3%
n3827
 
5.2%
A3699
 
5.1%
M3507
 
4.8%
r3335
 
4.6%
Other values (12)26298
36.1%
Common
ValueCountFrequency (%)
-24301
37.0%
914355
21.9%
85062
 
7.7%
04797
 
7.3%
72895
 
4.4%
42614
 
4.0%
52519
 
3.8%
62476
 
3.8%
12326
 
3.5%
32277
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII138557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-24301
17.5%
914355
 
10.4%
e6440
 
4.6%
J5706
 
4.1%
u5693
 
4.1%
a5590
 
4.0%
85062
 
3.7%
c4978
 
3.6%
04797
 
3.5%
p3830
 
2.8%
Other values (23)57805
41.7%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8703345541
Minimum0
Maximum8
Zeros11833
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.071179193
Coefficient of variation (CV)1.230767166
Kurtosis2.511495036
Mean0.8703345541
Median Absolute Deviation (MAD)1
Skewness1.378090416
Sum21150
Variance1.147424863
MonotonicityNot monotonic
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
011833
48.7%
16634
27.3%
23606
 
14.8%
31869
 
7.7%
4206
 
0.8%
586
 
0.4%
636
 
0.1%
721
 
0.1%
810
 
< 0.1%
ValueCountFrequency (%)
011833
48.7%
16634
27.3%
23606
 
14.8%
31869
 
7.7%
4206
 
0.8%
586
 
0.4%
636
 
0.1%
721
 
0.1%
810
 
< 0.1%
ValueCountFrequency (%)
810
 
< 0.1%
721
 
0.1%
636
 
0.1%
586
 
0.4%
4206
 
0.8%
31869
 
7.7%
23606
 
14.8%
16634
27.3%
011833
48.7%

open_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.347475413
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile18
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.398961333
Coefficient of variation (CV)0.4706042154
Kurtosis1.744522577
Mean9.347475413
Median Absolute Deviation (MAD)3
Skewness1.01349455
Sum227153
Variance19.35086081
MonotonicityNot monotonic
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
72493
10.3%
82398
9.9%
62388
9.8%
92316
9.5%
101978
 
8.1%
51941
 
8.0%
111683
 
6.9%
121410
 
5.8%
41373
 
5.6%
131187
 
4.9%
Other values (28)5134
21.1%
ValueCountFrequency (%)
2345
 
1.4%
3875
 
3.6%
41373
5.6%
51941
8.0%
62388
9.8%
72493
10.3%
82398
9.9%
92316
9.5%
101978
8.1%
111683
6.9%
ValueCountFrequency (%)
441
 
< 0.1%
411
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
361
 
< 0.1%
345
< 0.1%
332
 
< 0.1%
322
 
< 0.1%
315
< 0.1%
308
< 0.1%

pub_rec
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
0
23050 
1
 
1212
2
 
33
3
 
4
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24301
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
023050
94.9%
11212
 
5.0%
233
 
0.1%
34
 
< 0.1%
42
 
< 0.1%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
023050
94.9%
11212
 
5.0%
233
 
0.1%
34
 
< 0.1%
42
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
023050
94.9%
11212
 
5.0%
233
 
0.1%
34
 
< 0.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24301
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023050
94.9%
11212
 
5.0%
233
 
0.1%
34
 
< 0.1%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common24301
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023050
94.9%
11212
 
5.0%
233
 
0.1%
34
 
< 0.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII24301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023050
94.9%
11212
 
5.0%
233
 
0.1%
34
 
< 0.1%
42
 
< 0.1%

revol_bal
Real number (ℝ≥0)

ZEROS

Distinct16118
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13427.54738
Minimum0
Maximum149588
Zeros606
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile350
Q13794
median8906
Q317102
95-th percentile41844
Maximum149588
Range149588
Interquartile range (IQR)13308

Descriptive statistics

Standard deviation15801.73578
Coefficient of variation (CV)1.176814747
Kurtosis14.45815964
Mean13427.54738
Median Absolute Deviation (MAD)6014
Skewness3.144976502
Sum326302829
Variance249694853.6
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0606
 
2.5%
19
 
< 0.1%
238
 
< 0.1%
397
 
< 0.1%
4827
 
< 0.1%
7987
 
< 0.1%
527
 
< 0.1%
4527
 
< 0.1%
18216
 
< 0.1%
15756
 
< 0.1%
Other values (16108)23631
97.2%
ValueCountFrequency (%)
0606
2.5%
19
 
< 0.1%
23
 
< 0.1%
34
 
< 0.1%
43
 
< 0.1%
56
 
< 0.1%
64
 
< 0.1%
71
 
< 0.1%
83
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
1495881
< 0.1%
1488041
< 0.1%
1478971
< 0.1%
1477501
< 0.1%
1475591
< 0.1%
1474511
< 0.1%
1464721
< 0.1%
1457111
< 0.1%
1455181
< 0.1%
1447231
< 0.1%

revol_util
Categorical

HIGH CARDINALITY

Distinct1059
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
0%
 
592
63%
 
44
66.60%
 
42
0.20%
 
42
57.70%
 
40
Other values (1054)
23541 

Length

Max length6
Median length6
Mean length5.519484795
Min length0

Characters and Unicode

Total characters134129
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.2%

Sample

1st row1.20%
2nd row14%
3rd row34.40%
4th row9.40%
5th row2.80%

Common Values

ValueCountFrequency (%)
0%592
 
2.4%
63%44
 
0.2%
66.60%42
 
0.2%
0.20%42
 
0.2%
57.70%40
 
0.2%
46.40%39
 
0.2%
70.40%39
 
0.2%
68.70%39
 
0.2%
48.90%39
 
0.2%
57.40%38
 
0.2%
Other values (1049)23347
96.1%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
0592
 
2.4%
6344
 
0.2%
66.6042
 
0.2%
0.2042
 
0.2%
57.7040
 
0.2%
46.4039
 
0.2%
70.4039
 
0.2%
68.7039
 
0.2%
48.9039
 
0.2%
57.4038
 
0.2%
Other values (1048)23318
96.1%

Most occurring characters

ValueCountFrequency (%)
024288
18.1%
%24272
18.1%
.21327
15.9%
47486
 
5.6%
77380
 
5.5%
57370
 
5.5%
67342
 
5.5%
37296
 
5.4%
27035
 
5.2%
87010
 
5.2%
Other values (2)13323
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number88530
66.0%
Other Punctuation45599
34.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024288
27.4%
47486
 
8.5%
77380
 
8.3%
57370
 
8.3%
67342
 
8.3%
37296
 
8.2%
27035
 
7.9%
87010
 
7.9%
96670
 
7.5%
16653
 
7.5%
Other Punctuation
ValueCountFrequency (%)
%24272
53.2%
.21327
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common134129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024288
18.1%
%24272
18.1%
.21327
15.9%
47486
 
5.6%
77380
 
5.5%
57370
 
5.5%
67342
 
5.5%
37296
 
5.4%
27035
 
5.2%
87010
 
5.2%
Other values (2)13323
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII134129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024288
18.1%
%24272
18.1%
.21327
15.9%
47486
 
5.6%
77380
 
5.5%
57370
 
5.5%
67342
 
5.5%
37296
 
5.4%
27035
 
5.2%
87010
 
5.2%
Other values (2)13323
9.9%

total_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.14139336
Minimum2
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum2
5-th percentile7
Q114
median20
Q329
95-th percentile43
Maximum79
Range77
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.36428267
Coefficient of variation (CV)0.5132595989
Kurtosis0.6494638644
Mean22.14139336
Median Absolute Deviation (MAD)7
Skewness0.8207349499
Sum538058
Variance129.1469207
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16922
 
3.8%
15902
 
3.7%
17900
 
3.7%
14893
 
3.7%
20879
 
3.6%
18873
 
3.6%
13849
 
3.5%
21845
 
3.5%
12816
 
3.4%
19814
 
3.3%
Other values (63)15608
64.2%
ValueCountFrequency (%)
21
 
< 0.1%
395
 
0.4%
4253
 
1.0%
5331
1.4%
6413
1.7%
7509
2.1%
8606
2.5%
9665
2.7%
10696
2.9%
11746
3.1%
ValueCountFrequency (%)
792
< 0.1%
781
 
< 0.1%
752
< 0.1%
731
 
< 0.1%
721
 
< 0.1%
701
 
< 0.1%
691
 
< 0.1%
674
< 0.1%
664
< 0.1%
652
< 0.1%

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23403
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11968.51764
Minimum0
Maximum58480.13992
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile1923.16829
Q15598.284713
median9884.01
Q316204.15357
95-th percentile29675.9581
Maximum58480.13992
Range58480.13992
Interquartile range (IQR)10605.86886

Descriptive statistics

Standard deviation8770.861953
Coefficient of variation (CV)0.7328277583
Kurtosis2.098113535
Mean11968.51764
Median Absolute Deviation (MAD)4877.779336
Skewness1.348177624
Sum290846947.2
Variance76928019.39
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11196.5694319
 
0.1%
13435.9002110
 
< 0.1%
13263.9546410
 
< 0.1%
5478.3879819
 
< 0.1%
13148.137869
 
< 0.1%
10956.775969
 
< 0.1%
08
 
< 0.1%
5557.0255438
 
< 0.1%
11163.544737
 
< 0.1%
14346.479057
 
< 0.1%
Other values (23393)24205
99.6%
ValueCountFrequency (%)
08
< 0.1%
33.731
 
< 0.1%
44.921
 
< 0.1%
44.961
 
< 0.1%
66.771
 
< 0.1%
67.321
 
< 0.1%
69.641
 
< 0.1%
69.771
 
< 0.1%
76.331
 
< 0.1%
78.931
 
< 0.1%
ValueCountFrequency (%)
58480.139921
< 0.1%
56849.269861
< 0.1%
56199.439951
< 0.1%
55906.94991
< 0.1%
55768.779951
< 0.1%
54774.419921
< 0.1%
54714.75991
< 0.1%
54132.398081
< 0.1%
54111.739951
< 0.1%
54005.479961
< 0.1%

total_pymnt_inv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23267
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11367.89998
Minimum0
Maximum58438.37
Zeros109
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile1460.13
Q15149.52
median9263.59
Q315439.11
95-th percentile28971.85
Maximum58438.37
Range58438.37
Interquartile range (IQR)10289.59

Descriptive statistics

Standard deviation8652.249737
Coefficient of variation (CV)0.7611124091
Kurtosis2.148563125
Mean11367.89998
Median Absolute Deviation (MAD)4799.98
Skewness1.362242987
Sum276251337.3
Variance74861425.51
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0109
 
0.4%
6717.9511
 
< 0.1%
6514.5210
 
< 0.1%
11196.5710
 
< 0.1%
7173.248
 
< 0.1%
10956.788
 
< 0.1%
13148.148
 
< 0.1%
5478.398
 
< 0.1%
13263.957
 
< 0.1%
2171.517
 
< 0.1%
Other values (23257)24115
99.2%
ValueCountFrequency (%)
0109
0.4%
12.651
 
< 0.1%
21.61
 
< 0.1%
25.181
 
< 0.1%
26.191
 
< 0.1%
33.731
 
< 0.1%
40.041
 
< 0.1%
44.921
 
< 0.1%
44.961
 
< 0.1%
53.951
 
< 0.1%
ValueCountFrequency (%)
58438.371
< 0.1%
56515.161
< 0.1%
55867.021
< 0.1%
55579.281
< 0.1%
54675.681
< 0.1%
53954.41
< 0.1%
53928.331
< 0.1%
53118.311
< 0.1%
53018.621
< 0.1%
52847.961
< 0.1%

total_rec_prncp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4543
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9723.016746
Minimum0
Maximum35000.01
Zeros48
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile1363.97
Q14700
median8000
Q313250
95-th percentile24250
Maximum35000.01
Range35000.01
Interquartile range (IQR)8550

Descriptive statistics

Standard deviation6971.62505
Coefficient of variation (CV)0.7170228368
Kurtosis1.215660751
Mean9723.016746
Median Absolute Deviation (MAD)4000
Skewness1.139092078
Sum236279030
Variance48603555.84
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100001461
 
6.0%
120001177
 
4.8%
60001057
 
4.3%
50001051
 
4.3%
15000878
 
3.6%
8000798
 
3.3%
20000672
 
2.8%
4000582
 
2.4%
3000553
 
2.3%
7000538
 
2.2%
Other values (4533)15534
63.9%
ValueCountFrequency (%)
048
0.2%
21.931
 
< 0.1%
22.51
 
< 0.1%
24.871
 
< 0.1%
30.321
 
< 0.1%
34.51
 
< 0.1%
35.141
 
< 0.1%
35.81
 
< 0.1%
38.231
 
< 0.1%
40.231
 
< 0.1%
ValueCountFrequency (%)
35000.011
 
< 0.1%
35000222
0.9%
34999.991
 
< 0.1%
34999.971
 
< 0.1%
348001
 
< 0.1%
345251
 
< 0.1%
34475.011
 
< 0.1%
344752
 
< 0.1%
342501
 
< 0.1%
340008
 
< 0.1%

total_rec_int
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22197
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2143.968805
Minimum0
Maximum23480.14
Zeros45
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile186.74
Q1664.42
median1336.55
Q32711.22
95-th percentile6888.21
Maximum23480.14
Range23480.14
Interquartile range (IQR)2046.8

Descriptive statistics

Standard deviation2388.945182
Coefficient of variation (CV)1.114263032
Kurtosis9.956250843
Mean2143.968805
Median Absolute Deviation (MAD)838.1
Skewness2.671187185
Sum52100585.94
Variance5707059.085
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045
 
0.2%
1196.5719
 
0.1%
717.9513
 
0.1%
1148.1411
 
< 0.1%
514.5210
 
< 0.1%
1435.910
 
< 0.1%
1263.9510
 
< 0.1%
1500.7910
 
< 0.1%
956.7810
 
< 0.1%
478.399
 
< 0.1%
Other values (22187)24154
99.4%
ValueCountFrequency (%)
045
0.2%
6.221
 
< 0.1%
6.271
 
< 0.1%
7.22
 
< 0.1%
8.231
 
< 0.1%
9.581
 
< 0.1%
10.261
 
< 0.1%
11.171
 
< 0.1%
11.181
 
< 0.1%
11.231
 
< 0.1%
ValueCountFrequency (%)
23480.141
< 0.1%
22122.31
< 0.1%
21849.271
< 0.1%
21199.441
< 0.1%
20909.991
< 0.1%
20906.951
< 0.1%
20768.781
< 0.1%
19820.911
< 0.1%
19774.421
< 0.1%
19714.761
< 0.1%

total_rec_late_fee
Real number (ℝ≥0)

ZEROS

Distinct841
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.35800838
Minimum0
Maximum166.4297107
Zeros23074
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.41
Maximum166.4297107
Range166.4297107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.397470223
Coefficient of variation (CV)5.447293502
Kurtosis100.8881406
Mean1.35800838
Median Absolute Deviation (MAD)0
Skewness8.525174382
Sum33000.96164
Variance54.7225657
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023074
95.0%
15136
 
0.6%
15.0000000136
 
0.1%
15.0000000232
 
0.1%
3029
 
0.1%
14.9999999924
 
0.1%
15.0000000321
 
0.1%
14.9999999720
 
0.1%
14.9999999817
 
0.1%
15.0000000416
 
0.1%
Other values (831)896
 
3.7%
ValueCountFrequency (%)
023074
95.0%
0.011
 
< 0.1%
0.0607997511
 
< 0.1%
0.1017045621
 
< 0.1%
0.1399999991
 
< 0.1%
0.1800829041
 
< 0.1%
0.184773621
 
< 0.1%
0.271
 
< 0.1%
0.3020365531
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
166.42971071
< 0.1%
165.691
< 0.1%
146.60000031
< 0.1%
146.041
< 0.1%
130.59703721
< 0.1%
127.78781361
< 0.1%
120.811
< 0.1%
119.99999981
< 0.1%
119.071
< 0.1%
116.10000011
< 0.1%

recoveries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2551
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.1741605
Minimum0
Maximum29623.35
Zeros21677
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile389.54
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation719.3517881
Coefficient of variation (CV)7.181011395
Kurtosis378.0336495
Mean100.1741605
Median Absolute Deviation (MAD)0
Skewness16.41503055
Sum2434332.274
Variance517466.995
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021677
89.2%
10.073
 
< 0.1%
13.593
 
< 0.1%
24.062
 
< 0.1%
138.022
 
< 0.1%
220.232
 
< 0.1%
9.672
 
< 0.1%
9.772
 
< 0.1%
20.22
 
< 0.1%
139.622
 
< 0.1%
Other values (2541)2604
 
10.7%
ValueCountFrequency (%)
021677
89.2%
6.31
 
< 0.1%
8.191
 
< 0.1%
8.361
 
< 0.1%
8.461
 
< 0.1%
8.561
 
< 0.1%
8.881
 
< 0.1%
8.891
 
< 0.1%
9.091
 
< 0.1%
9.111
 
< 0.1%
ValueCountFrequency (%)
29623.351
< 0.1%
22943.371
< 0.1%
21810.311
< 0.1%
19508.261
< 0.1%
16268.351
< 0.1%
16066.021
< 0.1%
16008.021
< 0.1%
15914.931
< 0.1%
15236.311
< 0.1%
14906.811
< 0.1%

collection_recovery_fee
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1800
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.35402273
Minimum0
Maximum5602.72
Zeros21936
Zeros (%)90.3%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.49
Maximum5602.72
Range5602.72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation139.801427
Coefficient of variation (CV)11.31626759
Kurtosis671.2703989
Mean12.35402273
Median Absolute Deviation (MAD)0
Skewness22.72682334
Sum300215.1063
Variance19544.43899
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021936
90.3%
1.67
 
< 0.1%
0.87
 
< 0.1%
1.87
 
< 0.1%
2.026
 
< 0.1%
4.956
 
< 0.1%
3.716
 
< 0.1%
3.396
 
< 0.1%
4.735
 
< 0.1%
2.555
 
< 0.1%
Other values (1790)2310
 
9.5%
ValueCountFrequency (%)
021936
90.3%
0.0631
 
< 0.1%
0.1347999951
 
< 0.1%
0.13931
 
< 0.1%
0.19521
 
< 0.1%
0.2007000011
 
< 0.1%
0.231
 
< 0.1%
0.24621
 
< 0.1%
0.2516999991
 
< 0.1%
0.25431
 
< 0.1%
ValueCountFrequency (%)
5602.721
< 0.1%
5569.921
< 0.1%
5216.741
< 0.1%
4902.081
< 0.1%
4900.751
< 0.1%
4821.781
< 0.1%
4254.911
< 0.1%
3988.811
< 0.1%
3926.151
< 0.1%
3590.67951
< 0.1%

last_pymnt_d
Categorical

HIGH CARDINALITY

Distinct102
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
13-Mar
 
628
13-May
 
586
14-Dec
 
553
13-Feb
 
548
12-Aug
 
543
Other values (97)
21443 

Length

Max length6
Median length6
Mean length5.971523806
Min length0

Characters and Unicode

Total characters145114
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row11-Mar
2nd row10-Mar
3rd row11-Dec
4th row15-Sep
5th row10-May

Common Values

ValueCountFrequency (%)
13-Mar628
 
2.6%
13-May586
 
2.4%
14-Dec553
 
2.3%
13-Feb548
 
2.3%
12-Aug543
 
2.2%
14-Jan543
 
2.2%
14-Jul534
 
2.2%
12-Mar531
 
2.2%
14-Mar529
 
2.2%
14-Aug523
 
2.2%
Other values (92)18783
77.3%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
13-mar628
 
2.6%
13-may586
 
2.4%
14-dec553
 
2.3%
13-feb548
 
2.3%
12-aug543
 
2.2%
14-jan543
 
2.2%
14-jul534
 
2.2%
12-mar531
 
2.2%
14-mar529
 
2.2%
14-oct523
 
2.2%
Other values (91)18738
77.3%

Most occurring characters

ValueCountFrequency (%)
126987
18.6%
-24256
16.7%
a6318
 
4.4%
e6109
 
4.2%
u5983
 
4.1%
35940
 
4.1%
45836
 
4.0%
J5797
 
4.0%
25607
 
3.9%
M4401
 
3.0%
Other values (22)47880
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter48512
33.4%
Decimal Number48090
33.1%
Dash Punctuation24256
16.7%
Uppercase Letter24256
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6318
13.0%
e6109
12.6%
u5983
12.3%
r4343
9.0%
c4241
8.7%
p3907
8.1%
n3763
7.8%
g2103
 
4.3%
t2039
 
4.2%
l2034
 
4.2%
Other values (4)7672
15.8%
Decimal Number
ValueCountFrequency (%)
126987
56.1%
35940
 
12.4%
45836
 
12.1%
25607
 
11.7%
51586
 
3.3%
01137
 
2.4%
6575
 
1.2%
9337
 
0.7%
885
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
J5797
23.9%
M4401
18.1%
A4069
16.8%
D2202
 
9.1%
O2039
 
8.4%
F1966
 
8.1%
S1941
 
8.0%
N1841
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
-24256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72768
50.1%
Common72346
49.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6318
 
8.7%
e6109
 
8.4%
u5983
 
8.2%
J5797
 
8.0%
M4401
 
6.0%
r4343
 
6.0%
c4241
 
5.8%
A4069
 
5.6%
p3907
 
5.4%
n3763
 
5.2%
Other values (12)23837
32.8%
Common
ValueCountFrequency (%)
126987
37.3%
-24256
33.5%
35940
 
8.2%
45836
 
8.1%
25607
 
7.8%
51586
 
2.2%
01137
 
1.6%
6575
 
0.8%
9337
 
0.5%
885
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII145114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
126987
18.6%
-24256
16.7%
a6318
 
4.4%
e6109
 
4.2%
u5983
 
4.1%
35940
 
4.1%
45836
 
4.0%
J5797
 
4.0%
25607
 
3.9%
M4401
 
3.0%
Other values (22)47880
33.0%

last_pymnt_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22582
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2771.94458
Minimum0
Maximum36115.2
Zeros45
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size190.0 KiB

Quantile statistics

Minimum0
5-th percentile43.24
Q1223.36
median586.18
Q33514.26
95-th percentile12418.61
Maximum36115.2
Range36115.2
Interquartile range (IQR)3290.9

Descriptive statistics

Standard deviation4505.872171
Coefficient of variation (CV)1.62552751
Kurtosis8.524347287
Mean2771.94458
Median Absolute Deviation (MAD)497.19
Skewness2.654086317
Sum67361025.25
Variance20302884.02
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045
 
0.2%
20011
 
< 0.1%
40010
 
< 0.1%
509
 
< 0.1%
1009
 
< 0.1%
5008
 
< 0.1%
1508
 
< 0.1%
324.427
 
< 0.1%
2506
 
< 0.1%
276.066
 
< 0.1%
Other values (22572)24182
99.5%
ValueCountFrequency (%)
045
0.2%
0.011
 
< 0.1%
0.161
 
< 0.1%
0.241
 
< 0.1%
0.251
 
< 0.1%
0.281
 
< 0.1%
0.351
 
< 0.1%
0.362
 
< 0.1%
0.443
 
< 0.1%
0.511
 
< 0.1%
ValueCountFrequency (%)
36115.21
< 0.1%
35596.411
< 0.1%
35479.891
< 0.1%
35337.091
< 0.1%
35322.961
< 0.1%
35283.091
< 0.1%
35139.611
< 0.1%
34981.661
< 0.1%
34852.541
< 0.1%
34675.921
< 0.1%

last_credit_pull_d
Categorical

HIGH CARDINALITY

Distinct102
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
16-May
5932 
16-Apr
1526 
16-Mar
 
723
13-Feb
 
504
16-Feb
 
453
Other values (97)
15163 

Length

Max length6
Median length6
Mean length5.992839801
Min length5

Characters and Unicode

Total characters145632
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row11-Jul
2nd row10-Feb
3rd row11-Dec
4th row16-May
5th row10-May

Common Values

ValueCountFrequency (%)
16-May5932
24.4%
16-Apr1526
 
6.3%
16-Mar723
 
3.0%
13-Feb504
 
2.1%
16-Feb453
 
1.9%
15-Dec418
 
1.7%
16-Jan412
 
1.7%
15-Sep361
 
1.5%
13-Mar352
 
1.4%
14-Mar351
 
1.4%
Other values (92)13269
54.6%

Length

Histogram of lengths of the category
ValueCountFrequency (%)
16-may5932
24.4%
16-apr1526
 
6.3%
16-mar723
 
3.0%
13-feb504
 
2.1%
16-feb453
 
1.9%
15-dec418
 
1.7%
16-jan412
 
1.7%
15-sep361
 
1.5%
13-mar352
 
1.4%
14-mar351
 
1.4%
Other values (92)13269
54.6%

Most occurring characters

ValueCountFrequency (%)
125511
17.5%
-24301
16.7%
a10509
 
7.2%
M9208
 
6.3%
69046
 
6.2%
y7144
 
4.9%
e4748
 
3.3%
r4729
 
3.2%
p4018
 
2.8%
A3975
 
2.7%
Other values (23)42443
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter48602
33.4%
Decimal Number48428
33.3%
Dash Punctuation24301
16.7%
Uppercase Letter24301
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a10509
21.6%
y7144
14.7%
e4748
9.8%
r4729
9.7%
p4018
 
8.3%
u3659
 
7.5%
c2790
 
5.7%
n2374
 
4.9%
b1908
 
3.9%
o1417
 
2.9%
Other values (4)5306
10.9%
Decimal Number
ValueCountFrequency (%)
125511
52.7%
69046
 
18.7%
43862
 
8.0%
53593
 
7.4%
33205
 
6.6%
22514
 
5.2%
0523
 
1.1%
9136
 
0.3%
829
 
0.1%
79
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M9208
37.9%
A3975
16.4%
J3650
 
15.0%
F1908
 
7.9%
D1487
 
6.1%
N1417
 
5.8%
S1353
 
5.6%
O1303
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
-24301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72903
50.1%
Common72729
49.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a10509
14.4%
M9208
12.6%
y7144
 
9.8%
e4748
 
6.5%
r4729
 
6.5%
p4018
 
5.5%
A3975
 
5.5%
u3659
 
5.0%
J3650
 
5.0%
c2790
 
3.8%
Other values (12)18473
25.3%
Common
ValueCountFrequency (%)
125511
35.1%
-24301
33.4%
69046
 
12.4%
43862
 
5.3%
53593
 
4.9%
33205
 
4.4%
22514
 
3.5%
0523
 
0.7%
9136
 
0.2%
829
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII145632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125511
17.5%
-24301
16.7%
a10509
 
7.2%
M9208
 
6.3%
69046
 
6.2%
y7144
 
4.9%
e4748
 
3.3%
r4729
 
3.2%
p4018
 
2.8%
A3975
 
2.7%
Other values (23)42443
29.1%

pub_rec_bankruptcies
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size190.0 KiB
0.0
23303 
1.0
 
994
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72903
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.023303
95.9%
1.0994
 
4.1%
2.04
 
< 0.1%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
0.023303
95.9%
1.0994
 
4.1%
2.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
047604
65.3%
.24301
33.3%
1994
 
1.4%
24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48602
66.7%
Other Punctuation24301
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
047604
97.9%
1994
 
2.0%
24
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.24301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common72903
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
047604
65.3%
.24301
33.3%
1994
 
1.4%
24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII72903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
047604
65.3%
.24301
33.3%
1994
 
1.4%
24
 
< 0.1%

Interactions

Correlations

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statusdescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntlast_credit_pull_dpub_rec_bankruptcies
0010000100009950.000000367.51%311.11AA41510RENT30000.0Source Verified10-Sep0Borrower added on 08/30/10 > thank you<br/>home_improvementhomeimproement076xxNJ5.0001-Feb319014501.20%482247.2100002235.981509.91350.870.0386.433.9611-Mar311.1111-Jul0.0
11150001500014800.000000368.94%476.58AA514001RENT147000.0Not Verified10-Jan1Borrower added on 01/14/10 > Green city housing project<br/> Borrower added on 01/23/10 > I mistyped in the application. $1400 is the current rent we pay. <br/><br/>My employer is Emerson, and my role is marketing and biz dev director. Been with them for 3 years now. My 2009 annual compensation was ~160K. <br/><br/>I have zero credit card debt or car loans. Purpose of the loan is to buy real estate in my home town in India. It costs ~$140K. Rather than taking a mortgage there, I want to buy the property outright. I used $125K from my savings, and I am short $15K. The name of the building community is Green City - www.vplprojects.com.<br/>otherGreen City300xxGA3.470Feb-97060491014%1715112.76000014911.2615000.00112.760.00.000.0010-Mar15114.0310-Feb0.0
22200020002000.0000003613.57%67.94CC3360004OWN36000.0Not Verified9-Oct1major_purchasepayoff w/in 2 years310xxGA7.830Mar-00080179034.40%102354.9668272354.972000.00354.970.00.000.0011-Dec101.7811-Dec0.0
34140001400014000.0000006017.49%351.64DD5old palm inc5MORTGAGE50000.0Not Verified11-Jul1Borrower added on 06/29/11 > thanks for the help.<br/>debt_consolidationsave %334xxFL21.2414-Jun1915539.40%2720804.23002020804.2314000.006804.230.00.000.0015-Sep3943.2716-May0.0
45120001200011900.000000368.94%381.26AA5Brocade Communications7MORTGAGE294000.0Not Verified9-Dec1otherInvest in Lending Club Notes939xxCA0.500Aug-93011053062.80%2112344.81177012241.9412000.00344.810.00.000.0010-May11204.3010-May0.0
561000065256480.473624607.88%131.93AA5CenturyLink4MORTGAGE65000.0Not Verified10-Jun1Borrower added on 05/25/10 > I obtained a discover personal loan 2mths ago at a fixed rate of appx 18% for 72 mths to consolidate all revolving debt except 1 low rate card. This loan is to reduce both the term and rate of that loan in order to be debt free w/in 5 years. The only credit card balance I have is a 2nd Discover card which is a fixed balance transfer rate of 2.99% until 2013 which is being paid at a rate to accomplish $0 balance by 01/2013. Original debt was generated from costs to prepare home to sell which didn't sell. No other debt exists except fixed mortgage - not even a car payment.<br/>credit_cardDiscover640xxMO7.621Feb-961130562919.10%237908.8899977846.076525.001383.890.00.000.0015-Feb668.5315-Sep0.0
67780078007800.0000006014.91%185.20DD2Department of Homeland Security4MORTGAGE94000.0Verified11-Feb1Borrower added on 01/30/11 > Money will be used to make a lump some alimony payment.<br/> Borrower added on 01/30/11 > Lump sum alimony payment.<br/> Borrower added on 01/30/11 > Average monthly breakdown:<br/><br/>Mortgage: 1206<br/><br/>Total credit card payments: 1358<br/><br/>Car/Insurance: 300<br/><br/>Miscellaneous expenses: 300<br/>otherHelp With Divorce Expenses480xxMI12.960Feb-001110231737.40%379864.2895379864.297800.002064.290.00.000.0013-May58.5916-May0.0
78188251882518825.000000367.90%589.04AA4Down To Earth Distributors, Inc.5RENT42000.0Not Verified11-Oct1Borrower added on 10/03/11 > Loan to consolidate debt of a credit card and two student loans into one monthly payment. Thanks for considering investing in our financial health!<br/>debt_consolidationConsolidation Loan974xxOR22.740Aug-980702212155.40%2220958.52894020958.5318825.002133.530.00.000.0013-Dec239.1613-Dec0.0
810260026002600.0000003612.53%87.02BB5Plaid, Inc.2RENT40792.0Not Verified9-Oct1560296 added on 10/20/09 > I am a graphic designer and music producer in desperate need of a new computer for my career. I currently have an iMac that is several years old and has become almost unusable. I plan on using the money to buy one of the awesome new iMacs that came out today! This is my second LendingClub loan, I am a great borrower with no late payments!major_purchaseNew iMac for Work068xxCT13.5403-Nov1120984724.10%143132.3938513132.392600.00532.390.00.000.0012-Nov94.2616-May0.0
911200002000019725.0000006013.61%461.34CC2U.S. Dept. Of Homeland Security8MORTGAGE100000.0Verified10-Jul1debt_consolidationpaydown loan077xxNJ12.980Jan-9811502098427.50%2027679.64576027299.0520000.007679.650.00.000.0015-Jul513.1816-Apr0.0

Last rows

df_indexloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statusdescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntlast_credit_pull_dpub_rec_bankruptcies
2429124989500050004900.000003611.48%164.85BB2PPC Construction, Inc.8RENT48000.0Not Verified10-Jan1Borrower added on 01/12/10 > I plan to pay off all existing credit card debts and I have not missed a payment and always make them on time.<br/>debt_consolidationoperation debt free900xxCA14.5703-Mar0160747942.70%205934.5554875815.865000.0934.560.00.00.013-Feb178.6014-Feb0.0
2429224990132501325012950.000003610.99%433.73BB3PPC Mechanical Seals2MORTGAGE30000.0Source Verified11-Jun1debt_consolidationDebt Consolidation708xxLA5.8805-Sep060832850.80%1015556.78953015204.5613250.02306.790.00.00.014-Jan2590.8616-May0.0
2429324991130001300012950.00000365.42%392.08AA1PPD5MORTGAGE59000.0Not Verified11-Aug1Borrower added on 08/18/11 > I want to consolidate and pay off 3 credit cards that mainly grew quickly due to unforeseen major car issues and veterinarian bills. I'm a good borrower as I always make my monthly payments and have a steady job as an enterprise mobile app systems developer.<br/>debt_consolidationCredit Card Payoff284xxNC5.820Sep-930601333162.60%1214114.79470014060.5113000.01114.790.00.00.014-Sep401.7215-Dec0.0
2429424992850085008500.000006010.74%183.72BB4PPD3MORTGAGE38000.0Source Verified11-Feb1debt_consolidationCredit Card Consolidation284xxNC7.450Jul-93150893851.10%289990.1757269990.188500.01490.180.00.00.013-Jan5950.7716-Apr0.0
242952499314575145755700.000006013.99%339.06CC3PPD, Inc.10RENT45000.0Not Verified11-Jul1Borrower added on 07/21/11 > I plan on using the funds to pay off high interest credit cards and to have one payment a month. I am a good risk based on my credit score and that I have always been current on my debt obligations. As you can see I have been with the same company for 11 years and the company is very stable and growing.<br/> Borrower added on 07/22/11 > In addition, the monthly payment on this loan is less that what I currently have been paying monthly for all my credit cards. I plan on paying the required amount every month for the loan but will often pay more than that especially when I get items such as tax returns. bonuses and employee stock sales.<br/>debt_consolidationPersonal Loan284xxNC24.960Aug-9411901767029.20%4016478.7121906444.5014575.01903.710.00.00.012-Aug12752.5716-Mar0.0
2429624994550055005500.000003614.96%190.55DD2PPDG7MORTGAGE52000.0Not Verified9-Oct1554340 added on 10/10/09 > TRY TO PAY OFF ALL MY DEBT ASAP.I AM GETTING THERE SO THANKS FOR ALL YOUR SUPPORT ....LUMIdebt_consolidationFREEDOM320xxFL18.740Aug-9821101065558.20%376825.0667836825.075500.01325.070.00.00.012-May1122.2012-Jun0.0
2429724995114501145011450.00000368.49%361.40AA5PPG Industries7RENT40000.0Source Verified11-Jun1Borrower added on 06/18/11 > I've been at my job for 7 years now. I want to pay off my high interest credit cards.<br/> Borrower added on 06/21/11 > I've never been late on payments.<br/> Borrower added on 06/27/11 > I want to pay of my credit card debt fast so that I can start planning on saving for a house.<br/>debt_consolidationCredit Card Consolidation980xxWA21.2104-Jan01001019234%1513010.20603013010.2111450.01560.210.00.00.014-Jul382.9016-May0.0
2429824996240002400021100.314246020.53%642.96GG2PPG Industries10MORTGAGE74454.0Verified10-Aug1Borrower added on 08/21/10 > Consolidating all revolving debt to fixed debt and improve my great credit score to A1 status.<br/>debt_consolidationC3myboy301xxGA19.950Jun-983902910877.20%2433765.06831026978.9624000.09765.070.00.00.013-Jan16500.3916-May0.0
2429924997140001400011411.120893617.58%503.19FF2PPG Industries10MORTGAGE86000.0Verified9-May1I'm looking to consolidate high intrest loans and improve cash flow.debt_consolidationconsolidation loan 001440xxOH21.930Jul-9901311899195%2617917.01290014191.0614000.03917.010.00.00.011-Oct3857.8815-Jun1.0
2430024998134001340013400.000003613.06%451.89CC2PPG Industries, INC1RENT62040.0Not Verified11-Jan1Borrower added on 01/26/11 > I plan to use these funds to consolidate my credit cards that I used to pay off my relocation to my new job and buy furniture for the apartment. When I get this loan I will pay a lower interest than the amount I'm currently paying on my credit cards and will have a stable payment. Even when I was a student I've never missed a payment and because I'm building credit to later on buy a house I don't plan on doing it either. PPG industries, INC is a very stable company in which many of my co-workers have been working here over 30 or 40 years. The reason is that although there not the highest payers they are over the average and like I said there very stable. I work as a Mechanical Engineer developing projects. My income every month is $5,170 that subtracting taxes, health plan, 401k, saving account and all other deductions I get net of $3,900. I pay $950 monthly rent for my apartment and an average of $100 in utilities. Most of the payments that I'm making are for the credit cards, Mint.com suggested me to consolidate with you guys to save money and if all goes well I will.<br/>debt_consolidationRefinance Debt Loan706xxLA18.840Sep-003120538941.80%1416268.44055016268.4413400.02868.440.00.00.014-Feb475.2914-Feb0.0